• 경영과정보연구
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• 제3권
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• pp.15-45
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• 1999

Processing techniques of remote sensed image data using computer have been recognized very necessary techniques to all social fields, such as, environmental observation, land cultivation, resource investigation, military trend grasp and agricultural product estimation, etc. Especially, accurate classification and analysis to remote sensed image da are important elements that can determine reliability of remote sensed image data processing systems, and many researches have been processed to improve these accuracy of classification and analysis. Traditionally, remote sensed image data processing systems have been processed 2 or 3 selected bands in multiple bands, in this time, their selection criterions are statistical separability or wavelength properties. But, it have be bring up the necessity of bands selection method by data distribution characteristics than traditional bands selection by wavelength properties or statistical separability. Because data sensing environments change from multispectral environments to hyperspectral environments. In this paper for efficient data classification in multispectral bands environment, a band feature extraction method using the Rough sets theory is proposed. First, we make a look up table from training data, and analyze the properties of experimental multispectral image data, then select the efficient band using indiscernibility relation of Rough set theory from analysis results. Proposed method is applied to LANDSAT TM data on 2 June 1992. From this, we show clustering trends that similar to traditional band selection results by wavelength properties, from this, we verify that can use the proposed method that centered on data properties to select the efficient bands, though data sensing environment change to hyperspectral band environments.

• 한국측량학회지
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• 제35권5호
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• pp.349-356
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• 2017

In this study, we propose a method to investigate topographic characteristics by applying machine learning which is an artificial intelligence analysis method based on the spatial data constructed using UAV and the training data created through spatial analysis. This method provides an alternative to the subjective judgment and accuracy of spatial data, which is a problem of existing topographic characteristics survey for officially assessed land price. The analysis method of this study is expected to improve the problems of topographic characteristics survey method of existing field researchers and contribute to more accurate decision of officially assessed land price by providing more objective land survey method.

• Journal of information and communication convergence engineering
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• 제17권1호
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• pp.41-48
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• 2019

In recent years, sentiment analysis research has become popular. The research results of sentiment analysis have achieved remarkable results in practical applications, such as in Amazon's book recommendation system and the North American movie box office evaluation system. Analyzing big data based on user preferences and evaluations and recommending hot-selling books and hot-rated movies to users in a targeted manner greatly improve book sales and attendance rate in movies [1, 2]. However, traditional machine learning-based sentiment analysis methods such as the Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) had performed poorly in accuracy. In this paper, an improved kNN classification method is proposed. Through the improved method and normalizing of data, the purpose of improving accuracy is achieved. Subsequently, the three classification algorithms and the improved algorithm were compared based on experimental data. Experiments show that the improved method performs best in the kNN classification method, with an accuracy rate of 11.5% and a precision rate of 20.3%.

• 한국컴퓨터정보학회논문지
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• 제24권6호
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• pp.89-97
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• 2019

This study proposes a method for investigating current patents related to information communication technology and smart mobility to provide insights into future technology trends. The method is based on text mining clustering analysis. The method consists of two stages, which are data preparation and clustering analysis, respectively. In the first stage, tokenizing, filtering, stemming, and feature selection are implemented to transform the data into a usable format (structured data) and to extract useful information for the next stage. In the second stage, the structured data is partitioned into groups. The K-medoids algorithm is selected over the K-means algorithm for this analysis owing to its advantages in dealing with noise and outliers. The results of the analysis indicate that most current patents focus mainly on smart connectivity and smart guide systems, which play a major role in the development of smart mobility.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 추계학술대회논문집
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• pp.607-611
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• 2006

There are several methods to obtain structural vibration for analysis of vibro-acoustic noise. First of all, vibration data can be obtained through the structural analysis using finite element method. Although this method has no need to experiment, the analysis result is unreliable when the structure and the vibration source is complex to model exactly. The second method is to measure vibration using a number of sensors. The analyzed vibro-acoustic noise with directly measured data is setting morereliable when the number of data acquisition points is getting larger. However, it requires large amount of time and effort to measure all vibration data on every node especially when the size of vibrating structure is large. The Modal Expansion Method(MEM), which uses mode information and measurement data, has been introduced to compensate their limits. With a relatively small number of measurement data, the reliable structural vibration for vibro-acoustic noise can be obtained using this semi-analysis method. Although MEM gives reliable result, it is restricted by the number of modes and measurement points. In this paper, structural analysis, direct vibration measurement method and MEM are compared using the simple aluminum box model. Furthermore, the washing machine case is also provided as a comparative example. The Laser Doppler Vibrometer(LDV) was used instead of contact type accelerometer to get vibration data.

• 한국군사과학기술학회지
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• 제21권5호
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• pp.714-721
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• 2018

This study proposes a risk evaluation method based on RAM and AHP data in order to prevent subjectivity of risk assessment. The risk assessment consist of Risk Likelihood(RL) and Risk Consequence(RC) in five levels. However, risk analysis of warships is hard to make a judgment because of small quantity production(Ship), long building period, equipment changes, complexity, various kinds of equipments, etc. The proposed RAM data and AHP analysis method are used to quantify each level quantitatively. RAM(MTBF) date is used to classify the RL, and AHP analysis is used to classify the RC. These scientific and data-based method will increase objectivity as well as efficiency of risk evaluation.

• 한국멀티미디어학회논문지
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• 제12권12호
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• pp.1755-1760
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• 2009

In this research, for the first time, we tried to analyse Raman hyperspectral dentin data using Independent Component Analysis (ICA) to see its possibility of adoption for the dental analysis software. We captured hyperspectral dentin data on 569 spots on a molar with dental lesion by HR800 Micro Raman Spectrometer at UMKC-CRISP (University of Missouri at Kansas City-Center for Research on Interfacial Structure and Properties). Each spot has 1,005 hyperspectral data. We applied ICA to the captured hyperspectral data of dentin for evaluating ICA approach, and compared it with the well known multivariate analysis method, PCA. As a result of the experiment, ICA approach shows better local characteristic of dentin than the result of PCA. We confirmed that ICA also could be a good method along with PCA in the dental analysis software.

• 대한인간공학회:학술대회논문집
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• 대한인간공학회 1995년도 추계학술대회논문집
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• pp.13-17
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• 1995

A Psychophysical scaling method called magnitude estimation is frequently used to evaluate human sensation to physical stimuli. This paper described the procedure of magnitude estimation data analysis which consists of four modulus; reponse space, CMM(Cross-Modality Matdhing)/merge, standardization, scale building & data analysis method. This procedure is being developed as an expert system in which the four analysis modulus are programmed so that a novice user can perform the analysis.

• 디지털콘텐츠학회 논문지
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• 제16권4호
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• pp.545-553
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• 2015

모든 데이터는 그 자체로 가치를 가지고 있지만, 실세계에서 수집되는 데이터들은 무작위적이며 비구조화되어 있다. 따라서 이러한 데이터를 효율적으로 활용하기 위해서 데이터에서 유용한 정보를 추출하기 위한 데이터 변환과 분석 알고리즘들을 사용하게 된다. 이러한 목적으로 사용되는 것이 데이터 마이닝이다. 오늘날에는 데이터를 분석하기 위한 다양한 데이터 마이닝 기법뿐만 아니라, 대용량 데이터를 효율적으로 처리하기 위한 연산 요건과 빠른 분석 시간을 필요로 하고 있다. 대용량 데이터를 저장하기 위하여 하둡이 많이 사용되며, 이 하둡의 데이터를 분석하기 위하여 맵리듀스 프레임워크를 사용한다. 본 논문에서는 단일 머신에서 동작하는 알고리즘을 맵리듀스 프레임워크로 개발할 때 적용의 효율성을 높이기 위한 토너먼트 기반 적용 방안을 제안하였다. 본 방법은 다양한 알고리즘에 적용할 수 있으며, 널리 사용되는 데이터 마이닝 알고리즘인 k-means, k-근접 이웃 분류에 적용하여 그 유용성을 보였다.

• 한국지반공학회논문집
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• 제38권12호
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• pp.103-112
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• 2022

본 연구에서는 시간-침하량 계측 데이터를 기반으로 한 기존 침하 예측 이론식을 확인하였다. 기존 계측 기반 침하 예측 이론식 중 쌍곡선법 및 Asaoka법이 정확도가 높게 나타났으며, 이외 방법은 정확도가 낮은 것으로 확인되었다. 이러한 분석 결과를 토대로 기존 침하 예측 방법의 한계점을 도출하였으며, 이러한 한계점을 보완할 수 있는 개선방안으로써 가중 비선형 회귀분석을 통한 침하 예측 방법을 제시하였다.